Investigation of granite waste incorporated clay brick as a building material

Abstract The incorporation of various industrial waste materials as additives in the manufacture of clay-based products has been attracting a growing interest from researchers in recent years and is becoming common practice. The present work reports the changes in the behaviour of the clay material used in the brick industry due to additions of a granite sawing powder wastes, generated from ornamental stone processing industry in Madurai region, South India. The raw materials were characterized with respect to their chemical composition by XRF, mineralogical composition by XRD, particle size distribution and plasticity. Mixtures of clay and waste material (10-50 wt. %) were moulded by extrusion and sintered at temperature ranging from 600 to 900°C. Results of technological tests indicated that the granite waste proportion and firing temperature were the two key factors determining the quality of bricks. With 30 wt. % granite waste content, the reformulated briquette specimens sintered at 900°C exhibited better values of water absorption, porosity, bulk density and mechanical strength than the normal clay bricks produced in the industr

Assessing Methane Emissions from Rice Fields in Large Irrigation Projects Using Satellite-Derived Land Surface Temperature and Agronomic Flooding: A Spatial Analysis

Synthetic aperture radar (SAR) imagery, notably Sentinel-1A’s C-band, VV, and VH polarized SAR, has emerged as a crucial tool for mapping rice fields, especially in regions where cloud cover hinders optical imagery. Employing multi-temporal characteristics, SAR data were regularly collected and parameterized using MAPscape-Rice software, which integrates a fully automated processing chain to convert the data into terrain-geocoded σ° values. This facilitated the generation of rice area maps through a rule-based classifier approach, with classification accuracies ranging from 88.5 to 91.5 and 87.5 percent in 2017, 2018, and 2022, respectively. To estimate methane emissions, IPCC (37.13 kg/ha/season, 42.10 kg/ha/season, 43.19 kg/ha/season) and LST (36.05 kg/ha/season, 41.44 kg/ha/season, 38.07 kg/ha/season) factors were utilized in 2017, 2018 and 2022. Total methane emissions were recorded as 19.813 Gg, 20.661 Gg, and 25.72 Gg using IPCC and 19.155 Gg, 20.373 Gg, and 22.76 Gg using LST factors in 2017, 2018 and 2022. Overall accuracy in methane emission estimation, assessed against field observations, ranged from (IPCC) 85.71, 91.32, and 80.25 percent to (LST) 83.69, 91.43, and 84.69 percent for the years 2017, 2018 and 2022, respectively, confirming the efficacy of remote sensing in greenhouse gas monitoring and its potential for evaluating the impact of large-scale water management strategies on methane emissions and carbon credit-based ecosystem services at regional or national levels

A novel approach to evaluate the production kinetics of Extracellular Polymeric Substances (EPS) by activated sludge using weighted nonlinear least-squares analysis

This paper develops a novel and convenient approach for evaluation of production kinetics of extracellular polymeric substances (EPS) by activated sludge. In this approach, the weighted least-squares analysis is employed to calculate approximate differences in EPS concentration between model predictions and data. An iterative search routine in the Monte Carlo method is utilized for optimization of the objective function by minimizing the sum of squared weighted errors. Application of the approach in this work shows that the fraction of substrate electrons diverted to EPS formation (k) is 0.23 g COD g COD with a bacterial maximum growth rate of 0.32 h. The obtained parameters are found to be reasonable as they are generally bounded. The validity of this approach is confirmed by both the independent EPS production tests and the EPS data reported in literature. It also corrects the overestimation of cellular production and identifies that k is the key parameter in EPS production kinetics. Furthermore, this approach could estimate the kinetic parameters accurately using few data sets or even one set, which becomes very attractive for the processes where data are costly to obtain